Motion sensor data anonymization by time-frequency filtering

Noëlie Debs, Théo Jourdan, Ali Moukadem, Antoine Boutet, Carole Frindel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in wireless actimetry sensors allow recognizing human real-time activities with mobile devices. Although the analysis of data generated by these devices can have many benefits for healthcare, these data also contains private information about users without their awareness and may even cause their re-identification. In this paper, we propose a privacy-preserving framework for activity recognition. The method consists of a two-step process. First, acceleration signals are encoded in the time-frequency domain by three different linear transforms. Second, we propose a method to anonymize the acceleration signals by filtering in the time-frequency domain. Finally, we evaluate our approach for the three different linear transforms with a neural network classifier by comparing the performances for activity versus identity recognition. We extensively study the validity of our framework with a reference dataset: results show an accurate activity recognition (85%) while limiting the re-identifation rate (32%). This represents a large utility improvement (19%) against a slight privacy decrease (10%) compared to state-of-the-art baseline.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1707-1711
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 2021 Jan 24
Externally publishedYes
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 2020 Aug 242020 Aug 28

Publication series

NameEuropean Signal Processing Conference
Volume2021-January
ISSN (Print)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period20/8/2420/8/28

Keywords

  • Activity Recognition
  • Classification
  • Convolutional Neural Networks
  • Privacy
  • Time-Frequency

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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